Model Generation for Quantified Formulas: A Taint-Based Approach
Logic in Computer Science
2018-02-16 v1
Abstract
We focus in this paper on generating models of quantified first-order formulas over built-in theories, which is paramount in software verification and bug finding. While standard methods are either geared toward proving the absence of solution or targeted to specific theories, we propose a generic approach based on a reduction to the quantifier-free case. Our technique allows thus to reuse all the efficient machinery developed for that context. Experiments show a substantial improvement over state-of-the-art methods.
Keywords
Cite
@article{arxiv.1802.05616,
title = {Model Generation for Quantified Formulas: A Taint-Based Approach},
author = {Benjamin Farinier and Sébastien Bardin and Richard Bonichon and Marie-Laure Potet},
journal= {arXiv preprint arXiv:1802.05616},
year = {2018}
}